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Motivation: Network interpretation using correlations has several known difficulties. Firstly, the data structure has discrete counts with an excess of zeros creating non-normal non-continuous data. Secondly, correlations, often used as similarity measures in network inference, are not causal. Thirdly, there is a masking effect of mutualism on commensalism and competition on amensalism in ecological networks that interfere with interpretation (Faust and Raes, 2012). More explicitly, the symmetric nature of correlations (cor(X,Y)=cor(Y,X)) can mask the affect of the asymmetric ecology relationship (commensalism and amensalism). We aim to solve the third issue which may speed up targeted drug therapies or disease diagnosis based on specific relationships in gut microbiomes. Methods: We apply a non-symmetric correlation method, Gini Correlations which should serve as a better classifier of ecological relationships revealing a fuller picture of microbiomes. First, create simulated correlated and independent Zero-Inflated Negative Binomial data. Second, validate Gini correlations by comparing Gini with Pearson Spearman and Kendall correlations; calculate false positive rate, true positive rate, accuracy, ROC, AUC after applying Benjamini-Hochberg (1995) multiple testing correction. Simulation Result: Gini is consistent and out performs other methods for small sample sizes of 10 and 25 producing consistently low false positive rates across 64+ simulation settings as well as consistently high accuracy rates. When sample size is increased to 50 Gini performs as well as other methods. Real Data Result: For well-defined microbial communities Gini correlations found novel biologically and medically relevant relationships. However, Gini's ability to unmask non-symmetric ecological relationships is yet to be determined.

A monitoração de variáveis e o diagnóstico de falhas é um aspecto importante a se considerar seja em plantas nucleares ou indústrias de processos, pois um diagnóstico precoce de falha permite a correção do problema proporcionando a não interrupção da produção e a segurança do operador e, assim, não causando perdas econômicas. O objetivo deste trabalho é, dentro do universo de todas as variáveis monitoradas de um processo, construir um conjunto de variáveis, não necessariamente mínimo, que será a entrada de uma rede neural e, com isso, conseguir monitorar, o maior número possível de variáveis. Esta metodologia foi aplicada ao reator de pesquisas IEA-R1 do IPEN. Para isso, as variáveis Potência do reator, Vazão do primário, Posição de barras de controle/segurança e Diferença de pressão no núcleo do reator D P, foram agrupadas, pois por hipótese quase todas as variáveis monitoradas em um reator nuclear tem relação com alguma dessas ou pode ser resultado da interação de duas ou mais. Por exemplo, a Potência está relacionada ao aumento e diminuição de algumas temperaturas bem como à quantidade de radiação devido à fissão do urânio; as Barras são reguladoras de potência e, por conseqüência podem influenciar na quantidade de radiação e/ou temperaturas; a Vazão do Circuito Primário, responsável pelo transporte de energia e pela conseqüente retirada de calor do núcleo. Assim, tomando o grupo de variáveis mencionadas, calculamos a correlação existente entre este conjunto B e todas as outras variáveis monitoradas (coeficiente de correlação múltipla), isto é, através do cálculo da correlação múltipla, que é uma ferramenta proposta pela teoria das Correlações Canônicas, foi possível calcular o quanto o conjunto B pode predizer cada uma das variáveis monitoradas. Uma vez que não seja possível uma boa qualidade de predição com o conjunto B, é acrescentada uma ou mais variáveis que possuam alta correlação com a variável melhorando a qualidade de predição. Finalmente, uma rede pode ser treinada com o novo conjunto e os resultados quanto a monitoração foram bastante satisfatórios quanto às 64 variáveis monitoradas pelo sistema de aquisição de dados do reator IEA-R1 através de sensores e atuadores , pois com um conjunto de 9 variáveis foi possível monitorar 51 variáveis. / The monitoring of variables and diagnosis of sensor fault in nuclear power plants or processes industries is very important because an early diagnosis allows the correction of the fault and, like this, do not cause the production interruption, improving operators security and its not provoking economics losses. The objective of this work is, in the whole of all variables monitor of a nuclear power plant, to build a set, not necessary minimum, which will be the set of input variables of an artificial neural network and, like way, to monitor the biggest number of variables. This methodology was applied to the IEA-R1 Research Reactor at IPEN. For this, the variables Power, Rate of flow of primary circuit, Rod of control/security and Difference in pressure in the core of the reactor ( D P) was grouped, because, for hypothesis, almost whole of monitoring variables have relation with the variables early described or its effect can be result of the interaction of two or more. The Power is related to the increasing and decreasing of temperatures as well as the amount radiation due fission of the uranium; the Rods are controls of power and influence in the amount of radiation and increasing and decreasing of temperatures and the Rate of flow of primary circuit has function of the transport of energy by removing of heat of the nucleus Like this, labeling B= {Power, Rate of flow of Primary Circuit, Rod of Control/Security and D P} was computed the correlation between B and all another variables monitoring (coefficient of multiple correlation), that is, by the computer of the multiple correlation, that is tool of Theory of Canonical Correlations, was possible to computer how much the set B can predict each variable. Due the impossibility of a satisfactory approximation by B in the prediction of some variables, it was included one or more variables that have high correlation with this variable to improve the quality of prediction. In this work an artificial neural network was trained and the results were satisfactory since the IEA-R1 Data Acquisition System reactor monitors 64 variables and, with a set of 9 input variables resulting from the correlation analysis, it was possible to monitor 51 variables using neural networks.

A monitoração de variáveis e o diagnóstico de falhas é um aspecto importante a se considerar seja em plantas nucleares ou indústrias de processos, pois um diagnóstico precoce de falha permite a correção do problema proporcionando a não interrupção da produção e a segurança do operador e, assim, não causando perdas econômicas. O objetivo deste trabalho é, dentro do universo de todas as variáveis monitoradas de um processo, construir um conjunto de variáveis, não necessariamente mínimo, que será a entrada de uma rede neural e, com isso, conseguir monitorar, o maior número possível de variáveis. Esta metodologia foi aplicada ao reator de pesquisas IEA-R1 do IPEN. Para isso, as variáveis Potência do reator, Vazão do primário, Posição de barras de controle/segurança e Diferença de pressão no núcleo do reator D P, foram agrupadas, pois por hipótese quase todas as variáveis monitoradas em um reator nuclear tem relação com alguma dessas ou pode ser resultado da interação de duas ou mais. Por exemplo, a Potência está relacionada ao aumento e diminuição de algumas temperaturas bem como à quantidade de radiação devido à fissão do urânio; as Barras são reguladoras de potência e, por conseqüência podem influenciar na quantidade de radiação e/ou temperaturas; a Vazão do Circuito Primário, responsável pelo transporte de energia e pela conseqüente retirada de calor do núcleo. Assim, tomando o grupo de variáveis mencionadas, calculamos a correlação existente entre este conjunto B e todas as outras variáveis monitoradas (coeficiente de correlação múltipla), isto é, através do cálculo da correlação múltipla, que é uma ferramenta proposta pela teoria das Correlações Canônicas, foi possível calcular o quanto o conjunto B pode predizer cada uma das variáveis monitoradas. Uma vez que não seja possível uma boa qualidade de predição com o conjunto B, é acrescentada uma ou mais variáveis que possuam alta correlação com a variável melhorando a qualidade de predição. Finalmente, uma rede pode ser treinada com o novo conjunto e os resultados quanto a monitoração foram bastante satisfatórios quanto às 64 variáveis monitoradas pelo sistema de aquisição de dados do reator IEA-R1 através de sensores e atuadores , pois com um conjunto de 9 variáveis foi possível monitorar 51 variáveis. / The monitoring of variables and diagnosis of sensor fault in nuclear power plants or processes industries is very important because an early diagnosis allows the correction of the fault and, like this, do not cause the production interruption, improving operators security and its not provoking economics losses. The objective of this work is, in the whole of all variables monitor of a nuclear power plant, to build a set, not necessary minimum, which will be the set of input variables of an artificial neural network and, like way, to monitor the biggest number of variables. This methodology was applied to the IEA-R1 Research Reactor at IPEN. For this, the variables Power, Rate of flow of primary circuit, Rod of control/security and Difference in pressure in the core of the reactor ( D P) was grouped, because, for hypothesis, almost whole of monitoring variables have relation with the variables early described or its effect can be result of the interaction of two or more. The Power is related to the increasing and decreasing of temperatures as well as the amount radiation due fission of the uranium; the Rods are controls of power and influence in the amount of radiation and increasing and decreasing of temperatures and the Rate of flow of primary circuit has function of the transport of energy by removing of heat of the nucleus Like this, labeling B= {Power, Rate of flow of Primary Circuit, Rod of Control/Security and D P} was computed the correlation between B and all another variables monitoring (coefficient of multiple correlation), that is, by the computer of the multiple correlation, that is tool of Theory of Canonical Correlations, was possible to computer how much the set B can predict each variable. Due the impossibility of a satisfactory approximation by B in the prediction of some variables, it was included one or more variables that have high correlation with this variable to improve the quality of prediction. In this work an artificial neural network was trained and the results were satisfactory since the IEA-R1 Data Acquisition System reactor monitors 64 variables and, with a set of 9 input variables resulting from the correlation analysis, it was possible to monitor 51 variables using neural networks.

Little research has been done into implied correlations, and the small literature grows even smaller when referring to currency options. The existing literature has established that implied correlation is a good if not the best forecaster of future realized correlation, and that this ability to forecast is not necessarily universal. This paper will establish that the forecasting ability of implied correlations in currency options varies across currency pairs, thus proving that not all implied correlations are created equal. Using two different proxies for the quality of the forecaster, the paper attempts to explain which characteristics of an option on a currency pair affect the variation in forecasting ability.

Correlation filters (CFs) are well established and useful tools for a variety of tasks in signal processing and pattern recognition, including automatic target recognition and tracking, biometrics, landmark detection, and human action recognition. Traditionally, CFs have been designed and implemented efficiently in the frequency domain using the discrete Fourier transform (DFT). However, the element-wise multiplication of two DFTs in the frequency domain corresponds to a circular correlation, which results in aliasing (i.e., distortion) in the correlation output. Prior CF research has largely ignored these aliasing effects by making the assumption that linear correlation is approximated by circular correlation. In this work, we investigate in detail the topic of aliasing in CFs. First, we illustrate that the current formulation of CFs in the frequency domain is inherently flawed, as it unintentionally assumes circular correlation during the design phase. This means that existing CFs are not truly optimal. We introduce zero-aliasing correlation filters (ZACFs) which fix this formulation issue by ensuring that each CF formulation problem corresponds to a linear correlation rather than a circular correlation. By adopting the ZACF design modifications, we show that the recognition and localization performance of conventional CF designs can be significantly improved. We demonstrate these benefits using a variety of data sets and present solutions to the computational challenges associated with computing ZACFs. After a CF is designed, it is used for object recognition by correlating it with a test signal. We investigate the use of the well-known overlap-add (OLA) and overlap-save (OLS) algorithms to improve the computation and memory requirements of this correlation operation for high dimensional applications (e.g., video). Through this process, we highlight important tradeoffs between these two algorithms that have previously been undocumented. To improve the computation and memory requirements of OLA and OLS, we introduce a new block filtering scheme, denoted partial-aliasing OLA (PAOLA) that intentionally introduces aliasing into the output correlation. This aliasing causes conventional CFs to perform poorly. To remedy this, we introduce partial-aliasing correlation filters (PACFs), which are specifically designed to minimize this aliasing. We demonstrate through numerical results that PACFs outperform conventional CFs in the presence of aliasing.

Research pertaining to the distortion of the squared canonical correlation coefficient has traditionally been limited to the effects of sampling error and associated correction formulas. The purpose of this study was to compare the degree of attenuation of the squared canonical correlation coefficient under varying conditions of score reliability. Monte Carlo simulation methodology was used to fulfill the purpose of this study. Initially, data populations with various manipulated conditions were generated (N = 100,000). Subsequently, 500 random samples were drawn with replacement from each population, and data was subjected to canonical correlation analyses. The canonical correlation results were then analyzed using descriptive statistics and an ANOVA design to determine under which condition(s) the squared canonical correlation coefficient was most attenuated when compared to population Rc2 values. This information was analyzed and used to determine what effect, if any, the different conditions considered in this study had on Rc2. The results from this Monte Carlo investigation clearly illustrated the importance of score reliability when interpreting study results. As evidenced by the outcomes presented, the more measurement error (lower reliability) present in the variables included in an analysis, the more attenuation experienced by the effect size(s) produced in the analysis, in this case Rc2. These results also demonstrated the role between and within set correlation, variable set size, and sample size played in the attenuation levels of the squared canonical correlation coefficient.

De nombreux descripteurs sont utilisés pour décrire les composites tissés. Cela est dû en partie aux différents usages que l’on peut en faire. Par exemple, le descripteur le plus approprié pour une simulation mécanique (par exemple, un maillage d’éléments finis), n’est pas nécessairement le même que pour un algorithme de contrôle non destructif (par exemple, des descripteurs statistiques issus d’images tomographiques). Cette recherche propose de reconnaître que, malgré les nombreuses formes que peuvent prendre ces descripteurs textiles, le composite tissé 3D qui nous intéresse est intrinsèquement structuré. En fait, il est principalement défini par son motif de tissage 3D (arrangement des torons). Cette caractéristique commune à tous les descripteurs peut être exploitée pour “construire des liens” entre les nombreuses analyses différentes. Ceux-ci peuvent être obtenus à travers de la Corrélation d’Images Volumiques (DVC), qui fournit le champ de déplacement reliant toute paire de descripteurs. Les avantages de la DVC sont démultipliés en permettant la “relaxation” de l’hypothèse fondamentale de la conservation des coefficients d’absorption (le contraste tomographique), et l’utilisation d’une technique de régularisation mécanique “complète”. Cela se traduit par un algorithme de recalage robuste et rapide. Il permet de mesurer les “différences métriques” (déformations et distorsions du toron) et d’identifier les “différences topologiques” possibles (par exemple, les anomalies de tissage) qu’un textile peut connaitre. En somme, ce nouvel “cadre de corrélation” permets l’unification des descripteurs textiles dans un seul descripteur topologique. Divers échantillons tissés observés à l’échelle méso ont été étudiés dans ce contexte. / Many descriptors are employed for describing the woven composites. This is, in part, due to the different uses one may give them. For example, the most appropriate descriptor for a mechanical simulation (e.g., a finite element mesh), may not necessarily be the same as for a nondestructive testing algorithm (e.g., statistical descriptors issued from tomographic images). This research proposes to acknowledge that, despite the many forms that these textile descriptors may take, the 3D woven composite tat interests us is intrinsically structured. In fact, it is mainly defined by its 3D weaving pattern (arrangement of yarns). This common characteristic to all descriptors can be exploited to “construct bridges” between the many different analyses. These can be obtained by means of Digital Volume Correlation (DVC), which provides the displacement field relating any pair of descriptors. It should be noted that the advantages of DVC are multiplied by allowing for the “relaxation” of the fundamental assumption of conservation of absorption coefficients (i.e., the tomographic contrast), and the use of a “complete” mechanical regularization technique. This results in a robust and fast registration algorithm. It allows measuring the “metric differences” (yarn deformations and distortions) as well as identifying the possible “topological differences” (e.g., weaving anomalies) that a textile may undergo. In short, this new “correlation framework” allows the unification of the textile descriptors into a single topological descriptor. Various woven samples observed at the meso-scale were studied in this context.

L'analyse quantitative de volumes 3D obtenus par tomographie permet l’identification et la validation de modèles. La séquence d’analyse consiste en trois problèmes inverses successifs : (i) reconstruction des volumes (ii) mesure cinématique par corrélation d'images volumiques (DVC) et (iii) identification. Les très longs temps d’acquisition nécessaires interdisent de capter des phénomènes rapides. Une méthode de mesures, Projection-based Digital Volume Correlation (P-DVC), raccourcit la séquence précédente en identifiant les quantités clés sur les projections. Cette technique réduit jusqu'à 2 le nombre de radiographies utilisées pour le suivi de l’essai au lieu de 500 à 1000. Cette thèse étend cette approche en réduisant la quantité d’informations acquises, rendant ainsi accessibles des phénomènes de plus en plus rapides et repoussant les limites de la résolution temporelle. Deux axes ont ainsi été développés : - d’une part, l'utilisation de différentes régularisations, spatiales et temporelles des champs 4D (espace/temps) mesurés généralise la méthode P-DVC (avec volume de référence) à l'exploitation d’une seule radiographie par étape de chargement. L’essai peut désormais être réalisé de façon continue, en quelques minutes au lieu de plusieurs jours; - d’autre part, la mesure du mouvement peut être utilisée pour corriger le volume reconstruit lui-même. Cette observation conduit à proposer une nouvelle procédure de co-détermination du volume et de sa cinématique (sans prérequis), ce qui ouvre ainsi de nouvelles perspectives pour l’imagerie des matériaux et médicale où parfois le mouvement ne peut pas être interrompu. Le développement de ces deux axes permet d’envisager de nouvelles façons de réaliser les essais, plus rapides et plus centrés sur l’identification de quantités clés. Ces méthodes sont compatibles avec les récents développements « instrumentaux » de la tomographie rapide en synchrotron ou laboratoire, et permettent de réduire de plusieurs ordres de grandeurs les temps d’acquisition et les doses de rayonnement. / The quantitative analysis of 3D volumes obtained from tomography allows models to be identified and validated. It consists of a sequence of three successive inverse problems: (i) volume reconstruction (ii) kinematic measurement from Digital Volume Correlation (DVC) and (iii) identification. The required very long acquisition times prevent fast phenomena from being captured.A measurement method, called Projection-based DVC (P-DVC), shortens the previous sequence and identifies the kinematics directly from the projections. The number of radiographs needed for tracking the time evolution of the test is thereby reduced from 500 to 1000 down to 2.This thesis extends this projection-based approach to further reduce the required data, letting faster phenomena be captured and pushing the limits of time resolution. Two main axes were developed:- On the one hand, the use of different spatial and temporal regularizations of the 4D fields (space/time) generalizes the P-DVC approach (with a known reference volume) to the exploitation of a single radiograph per loading step. Thus, the test can be carried out with no interruptions, in a few minutes instead of several days.- On the other hand, the measured motion can be used to correct the reconstructed volume itself. This observation leads to the proposition of a novel procedure for the joint determination of the volume and its kinematics (without prior knowledge) opening up new perspectives for material and medical imaging where sometimes motion cannot be interrupted.end{itemize}The development of these two axes opens up new ways of performing tests, faster and driven to the identification of key quantities of interest. These methods are compatible with the recent ``hardware" developments of fast tomography, both at synchrotron beamlines or laboratory and save several orders of magnitude in acquisition time and radiation dose.

No description available.